Plug & play vs. one-time denoising.   Since we train our denoiser to establish a mapping function between DMR and its residual towards the ground truth, the first experiment we investigated is how/when to use this denoiser. We compare two frameworks, i.e., the plug & play [1]} and the one-time denoising. The plug & play framework decouples the forward physical model and the denoising prior using the ADMM technique [2]}. For one time denoising, we apply the residual denoiser once after the DMR has converged. One-time denoising is considered because it is considerably faster than plug & play. Our experimental results show that one-time denoising performs similar or even better than plug & play, shown in Table REF . We reason that this is related to our training process and the initialization of the high-res tensor. Our differentiable model involves a temporal transition process from an existing frame to a future frame. We initialize the high-res tensor with the reference intensity frame. In each DMR iteration, the reconstruction process produces artifacts that are similar to the degradations in the initialized image. However, our denoiser is trained to “recognize” this degradation and remove these artifacts. Therefore, our denoiser is most useful and efficient when applied after the DMR has convergedVisual results are included in supplementary material..
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